Non-Contact Blood Pressure Monitoring Using BCG and Vessel Imaging
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Solution Overview
Problem
Existing blood pressure monitoring technologies face limitations such as physical burden, errors due to contact, and high costs or complications from invasive methods, and non-invasive methods like sphygmomanometers have limitations in continuous measurement and accuracy.
Innovation Solution
A non-contact monitoring apparatus using a blood vessel deformation detection sensor and a ballistocardiogram sensor to measure blood volume and heartbeat changes, combined with machine learning to derive blood pressure values, including a blood vessel analysis unit, ballistocardiogram analysis unit, calculation unit, and machine learning unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contact-based sensors are used for blood pressure monitoring, then measurement can be performed, but it causes physical burden and changes sleeping environment
Solution Approach 1:
The patent replaces contact-based mechanical sensors with non-contact optical sensors. The imaging sensor captures blood vessel images optically without physical contact, and the ballistocardiogram sensor detects heartbeat mechanically through body movement rather than direct contact. This substitution eliminates the physical burden and sleeping environment disruption while maintaining measurement capability.
Solution Approach 2:
The patent introduces an imaging sensor as an intermediary to detect blood vessel changes. Instead of direct contact between sensor and body, the imaging sensor captures optical images of blood vessels, which then serve as intermediate data for deriving blood pressure information. This intermediary approach enables measurement without direct contact.
2Measurement precision
If sphygmomanometer cuff method is used, then blood pressure can be measured, but continuous measurement is not possible and errors occur depending on cuff size and measurer skill
Solution Approach 1:
The patent enables continuous blood pressure monitoring by using sensors that can operate continuously without interruption. The imaging sensor can continuously capture blood vessel images, and the ballistocardiogram sensor can continuously detect heartbeat signals, allowing for uninterrupted measurement unlike the intermittent sphygmomanometer method.
Solution Approach 2:
The system performs automated analysis of blood vessel images and ballistocardiogram signals to derive blood pressure values without requiring operator intervention. The processing units automatically process the sensor data, eliminating dependence on measurer skill and cuff size selection, making the system self-sufficient and operator-independent.
3Measurement precision
If catheter insertion method is used, then invasive blood pressure measurement can be performed, but cost is high and complications may occur
Solution Approach 1:
The patent replaces invasive mechanical catheter insertion with non-contact optical and mechanical sensing. The imaging sensor and ballistocardiogram sensor detect physiological signals externally without penetrating the body, eliminating the risk of infection, bleeding, and other complications associated with catheter insertion while maintaining measurement accuracy through sophisticated signal processing.
Solution Approach 2:
The patent creates optical copies (images) of blood vessels and mechanical copies (ballistocardiogram signals) of heartbeat movements. These copies serve as proxies for direct internal measurement, allowing blood pressure derivation without actual catheter insertion into blood vessels, thus avoiding all associated risks and costs.
4Ease of operation
If non-contact sensors are used, then user burden is reduced, but measurement accuracy may be compromised
Solution Approach 1:
The patent merges multiple non-contact sensing modalities - imaging sensor for blood vessel monitoring and ballistocardiogram sensor for heartbeat detection. By combining data from these different non-contact sources, the system compensates for individual sensor limitations and achieves accurate blood pressure measurement while maintaining user convenience.
Solution Approach 2:
The patent transforms physiological parameters (blood vessel image changes, ballistocardiogram signals) into blood pressure information through sophisticated processing. By changing and analyzing multiple parameters from non-contact sensors, the system derives accurate blood pressure values without requiring direct contact, thus maintaining both accuracy and user comfort.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables convenient and continuous blood pressure measurement with reduced user burden and improved accuracy by using non-contact sensors and machine learning to derive precise blood pressure values.
Implementation Method 1
a ballistocardiogram sensor unit installed adjacent to the user's back to measure the user's heartbeat
Implementation Method 2
the blood vessel deformation detection sensor unit may be equipped with an imaging sensor for imaging blood vessels on a part of the user's body
Data Source
AI summary
An apparatus for blood pressure monitoring comprises: a blood vessel deformation detection sensor unit for monitoring blood volume changes and blood flow in blood vessels of a part of the user's body; a ballistocardiogram sensor unit installed adjacent to the user's back to measure the user's heartbeat; a blood vessel analysis unit for receiving signals from the blood vessel deformation detection sensor unit, analyzing them, and generating a graph; a ballistocardiogram analysis unit for receiving signals from the ballistocardiogram sensor unit, analyzing them, and generating a graph; a calculation unit for receiving data from the blood vessel analysis unit and the ballistocardiogram analysis unit and deriving at least one parameter; and a machine learning unit for receiving data on the parameter from the calculation unit and performing machine learning to derive blood pressure values.